Transcript An Interval Approach to Discover Knowledge from Multiple

An Interval Approach to Discover
Knowledge from Multiple Fuzzy Estimations
Vagan Terziyan* & **,
Seppo Puuronen**, Helen Kaikova*
*Department of Artificial Intelligence and Information Systems,
Kharkov State Technical University of Radioelectronics, UKRAINE
e-mail: [email protected], [email protected]
**Department of Computer Science and Information Systems,
University of Jyvaskyla, FINLAND, e-mail: [email protected]
GRWS’98 - The 5-th Open German-Russian Workshop on Pattern Recognition and
Image Understanding, Herrsching, Germany, 21-25 September, 1998
Triangle of Friendship
University of Jyväskylä
Finland
State Technical University
of Radioelectronics
Kharkov
Ukraine
Herrsching,
GRWS’98 Host
Metaintelligence Laboratory: Research Topics
• Knowledge and metaknowledge engineering;
• Multiple experts;
• Context in Artificial Intelligence;
• Data Mining and Knowledge Discovery;
• Temporal Reasoning;
• Metamathematics;
• Semantic Balance and Medical Applications;
• Distance Education and Virtual Universities.
Contents
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Context in Pattern Recognition
Interval estimation
Decontextualization with two intervals
Decontextualization with several intervals
Trends of uncertainty
Interval estimation with several trends
Context in Pattern Recognition
pattern
Context 2
Context 3
Context 4
Decontextualization
Context 1
recognition
result
Decontextualization of Noise in Pattern
Recognition with Multiple Estimations
noise
estimations
1
pattern
2
3
4
Decontextualization
1
2
3
4
result
recognized
pattern
The Problem of Interval
Estimation
• Measurements (as well as expert opinions) are not
absolutely accurate.
• The measurement result is expected to lie in the interval
around the actual value.
• The inaccuracy leads to the need to estimate the
resulting inaccuracy of data processing.
• When experts are used to estimate the value of some
parameter, intervals are commonly used to describe
degrees of belief.
Noise of an Interval Estimation
• In many real life cases there is also some noise which does not
allow direct measurement of parameters.
• The noise can be considered as an undesirable effect (context) to
the evaluation of a parameter.
• Different measurement instruments as well as different experts
possess different resistance against the influence of noise.
• Using measurements from several different instruments as well as
estimations from multiple experts we try to discover the effect
caused by noise and thus be able to derive the decontextualized
measurement result.
Basic Assumption
• The estimation of some parameter x given by
more accurate knowledge source (i.e. source
guarantees smaller upper bound of measurement error) is
supposed to be closer to the actual value of
parameter x (i.e. source is more resistant against a noise of
estimation).
• The assumption allows us to derive different
trends in cases when there are multiple
estimations that result to shorter estimation
intervals.
Physical Interpretation of Decontextualization
R1
real
pattern
recognized
pattern
R2
R1  R2
Rres 
R1  R2
real
pattern
Rres
recognized
pattern
Uncertainty is like a
“resistance” for precise
recognition of a pattern
Conclusion
• If you have several opinions (estimations,
recognition results, solutions etc.) with
different value of uncertainty you can select
the most precise one,
however
• it seems more reasonable to order opinions
from the worst to the best one and try to
recognize a trend of uncertainty which helps
you to derive opinion more precise than the
best one.